Wearable device and operation method of the wearable device

ABSTRACT

A wearable device is disclosed. The wearable device may process a state variable defined based on motion information of a user, determine an interactive mode of the wearable device based on a gain associated with a magnitude of a torque of the wearable device, select a motion type from among motion types of the determined interactive mode based on a gait parameter of the user, determine a control factor for the torque based on the selected motion type, and generate the torque based on the processed state variable, the gain, and the determined control factor.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority under 35 U.S.C. § 119 to Korean PatentApplication No. 10-2019-0117551 filed on Sep. 24, 2019, in the KoreanIntellectual Property Office, the entire contents of which areincorporated herein by reference in their entirety.

BACKGROUND 1. Field

At least one example embodiment relates to a wearable device.

2. Description of the Related Art

A recent issue of aging societies has contributed to a growing number ofpeople who experience inconvenience and pain from reduced muscularstrength or joint problems due to aging. Thus, there is a growinginterest in walking assistance devices that enable elderly users orpatients with reduced muscular strength or joint problems to walk withless effort. In addition, exercise assistance devices that may helpincrease muscular strength of human bodies are under development.

SUMMARY

Some example embodiments relate to an operation method of a wearabledevice.

In some example embodiment, the operation method may include processinga state variable defined based on motion information of a user,determining an interactive mode of the wearable device based on a gainassociated with a magnitude of a torque of the wearable device,selecting a motion type from among motion types of the determinedinteractive mode based on a gait parameter of the user, determining acontrol factor for the torque based on the selected motion type, andgenerating the torque based on the processed state variable, the gain,and the determined control factor.

The processing of the state variable may include smoothing the statevariable.

In response to the gain being greater than or equal to a reference valueand being a positive number, the determining of the interactive mode mayinclude selecting a first interactive mode that assists the user in amovement of the user. In response to the gain being greater than orequal to the reference value and being a negative number, thedetermining of the interactive mode may include selecting a secondinteractive mode that applies resistance to a movement of the user. Inresponse to the gain being less than the reference value, thedetermining of the interactive mode may include selecting a thirdinteractive mode that applies high resistance to a movement of the user.

In response to a first gait feature value in the gait parameter beingless than or equal to a first threshold value, the selecting of themotion type may include determining the motion type of the wearabledevice to be a walk motion type. In response to the first gait featurevalue being greater than the first threshold value and less than orequal to a second threshold value, the selecting of the motion type mayinclude determining the motion type to be a walk-to-run motion type. Inresponse to the first gait feature value being greater than the secondthreshold value, the selecting of the motion type may includedetermining the motion type to be a run motion type.

The first gait feature value may include a cadence of the user.

In response to a second gait feature value in the gait parameter beinggreater than a third threshold value, the selecting of the motion typemay include determining the motion type of the wearable device to be ahigh-resistance motion type. In response to the second gait featurevalue being less than a fourth threshold value, the selecting of themotion type may include determining the motion type to be a slow motiontype.

The second gait feature value may include a mean value of angular curvelengths of both hip joints of the user during a preset period of time.

In response to a motion type change event occurring by the selecting ofthe motion type from among the motion types, the determining of thecontrol factor may include adjusting at least one of a smoothing factorto be used to smooth a signal obtained by sensing a movement of theuser, or a delay in output timing of the torque.

In response to the motion type change event occurring by the selectingof the walk motion type from among the motion types, the adjusting mayinclude decreasing the smoothing factor and increasing the delay.

In response to the motion type change event occurring by the selectingof the run motion type from among the motion types, the adjusting mayinclude increasing the smoothing factor and decreasing the delay.

The generating of the torque may include applying, to the processedstate variable, the gain, the determined control factor, and acompensation factor, and generating the torque based on a result of theapplying.

The motion information may include angles of both hip joints of theuser.

Some example embodiments relate to a wearable device.

In some example embodiment, the wearable device may include a controllerconfigured to process a state variable defined based on motioninformation of a user, determine an interactive mode of the wearabledevice based on a gain associated with a magnitude of a torque of thewearable device, select a motion type from among motion types of thedetermined interactive mode based on a gait parameter of the user,determine a control factor for the torque based on the selected motiontype, and control a driver based on the processed state variable, thegain, and the determined control factor, and the driver configured togenerate the torque under the control of the controller.

The controller may be configured to smooth the state variable.

In response to the gain being greater than or equal to a reference valueand being a positive number, the controller may be configured to selecta first interactive mode that assists the user in a movement of theuser. In response to the gain being greater than or equal to thereference value and being a negative number, the controller may beconfigured to select a second interactive mode that applies resistanceto a movement of the user. In response to the gain being less than thereference value, the controller may be configured to select a thirdinteractive mode that applies high resistance to a movement of the user.

In response to a first gait feature value in the gait parameter beingless than or equal to a first threshold value, the controller may beconfigured to determine the motion type of the wearable device to be awalk motion type. In response to the first gait feature value beinggreater than the first threshold value and less than or equal to asecond threshold value, the controller may be configured to determinethe motion type to be a walk-to-run motion type. In response to thefirst gait feature value being greater than the second threshold value,the controller may be configured to determine the motion type to be arun motion type.

The first gait feature value may include a cadence of the user.

In response to a second gait feature value in the gait parameter beinggreater than a third threshold value, the controller may be configuredto determine the motion type of the wearable device to be ahigh-resistance motion type. In response to the second gait featurevalue being less than a fourth threshold value, the controller may beconfigured to determine the motion type to be a slow motion type.

The second gait feature value may include a mean value of angular curvelengths of both hip joints of the user during a preset period of time.

In response to a motion type change event occurring by the selecting ofthe motion type from among the motion types, the controller may beconfigured to adjust at least one of a smoothing factor to be used tosmooth a signal obtained by sensing a movement of the user, or a delayin output timing of the torque.

In response to the motion type change event occurring by the selectingof the walk motion type from among the motion types, the controller maybe configured to decrease the smoothing factor and increase the delay.

In response to the motion type change event occurring by the selectingof the run motion type from among the motion types, the controller maybe configured to increase the smoothing factor and decrease the delay.

The controller may be configured to apply, to the processed statevariable, the gain, the determined control factor, and a compensationfactor.

The motion information may include angles of both hip joints of theuser.

Additional aspects of example embodiments will be set forth in part inthe description which follows and, in part, will be apparent from thedescription, or may be learned by practice of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other aspects will become apparent and more readilyappreciated from the following description of example embodiments, takenin conjunction with the accompanying drawings of which:

FIGS. 1 through 3 are diagrams illustrating an example of a wearabledevice according to at least one example embodiment;

FIGS. 4 through 7 are diagrams illustrating an example of an operationof a wearable device according to at least one example embodiment;

FIG. 8 is a diagram illustrating an example of a second gait featurevalue according to at least one example embodiment;

FIG. 9 is a diagram illustrating an example of a state machine of awearable device according to at least one example embodiment;

FIG. 10 is a flowchart illustrating an example of an operation method ofa wearable device according to at least one example embodiment; and

FIG. 11 is a diagram illustrating an example of a wearable deviceaccording to at least one example embodiment.

DETAILED DESCRIPTION

Hereinafter, some example embodiments will be described in detail withreference to the accompanying drawings. Regarding the reference numeralsassigned to the elements in the drawings, it should be noted that thesame elements will be designated by the same reference numerals,wherever possible, even though they are shown in different drawings.Also, in the description of embodiments, detailed description ofwell-known related structures or functions will be omitted when it isdeemed that such description will cause ambiguous interpretation of thepresent disclosure.

It should be understood, however, that there is no intent to limit thisdisclosure to the particular example embodiments disclosed. On thecontrary, example embodiments are to cover all modifications,equivalents, and alternatives falling within the scope of the exampleembodiments. Like numbers refer to like elements throughout thedescription of the figures.

In addition, terms such as first, second, A, B, (a), (b), and the likemay be used herein to describe components. Each of these terminologiesis not used to define an essence, order or sequence of a correspondingcomponent but used merely to distinguish the corresponding componentfrom other component(s). It should be noted that if it is described inthe specification that one component is “connected,” “coupled,” or“joined” to another component, a third component may be “connected,”“coupled,” and “joined” between the first and second components,although the first component may be directly connected, coupled orjoined to the second component.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the,” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises,” “comprising,”“includes,” and/or “including,” when used herein, specify the presenceof stated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which the disclosure of this applicationpertains. Terms, such as those defined in commonly used dictionaries,are to be interpreted as having a meaning that is consistent with theirmeaning in the context of the relevant art, and are not to beinterpreted in an idealized or overly formal sense unless expressly sodefined herein.

Also, in the description of example embodiments, detailed description ofstructures or functions that are thereby known after an understanding ofthe disclosure of the present application will be omitted when it isdeemed that such description will cause ambiguous interpretation of theexample embodiments.

Various example embodiments will now be described more fully withreference to the accompanying drawings in which some example embodimentsare shown. In the drawings, the thicknesses of layers and regions areexaggerated for clarity.

Hereinafter, examples will be described in detail with reference to theaccompanying drawings, and like reference numerals in the drawings referto like elements throughout.

FIGS. 1 through 3 are diagrams illustrating an example of a wearabledevice according to at least one example embodiment.

Referring to FIG. 1 , a wearable device 110 may sense or obtain motioninformation of a user 120 and generate a torque based on the sensed orobtained motion information and various factors. For example, thewearable device 110 may generate an assistance torque to assist the user120 in walking. For another example, the wearable device 110 maygenerate a resistance torque to apply resistance to the user 120 whilewalking. The generating of a torque of the wearable device 110 will bedescribed hereinafter in greater detail with reference to FIG. 4 .

The wearable device 110 may be provided in a hip type to be worn on ahip joint or a thigh of the user 120, an ankle type to be worn on anankle of the user 120, or a knee type to be worn on a knee of the user120, for example. However, a type of the wearable device 110 is notlimited to the examples described in the foregoing. The wearable device110 illustrated in FIGS. 2 and 3 is of a hip type.

Referring to FIGS. 2 and 3 , drivers 210-1 and 210-2 of the wearabledevice 110 are positioned around hip joints of the user 120, and acontroller 310 of the wearable device 110 is positioned around a waistof the user 120. That is, the wearable device 110 of a hip type may bedesigned such that the drivers 210-1 and 210-2 are positioned around thehip joints of the user 120 and the controller 310 is positioned aroundthe waist of the user 120. However, positions of the drivers 210-1 and210-2 and the controller 310 are not limited to the example positionsillustrated in FIGS. 2 and 3 .

FIGS. 4 through 7 are diagrams illustrating an example of an operationof the wearable device 110 according to at least one example embodiment.

Referring to FIG. 4 , in operation 410, the wearable device 110 senses amotion of the user 120 using a sensor. That is, the wearable device 110obtains motion information of the user 120. The motion information mayinclude, for example, angles of both hip joints of the user 120. Asillustrated in FIG. 5 , the wearable device 110 senses or obtains anangle q_(l)(t) of a left hip joint of the user 120 using an encoderpositioned around the driver 210-1, and senses or obtains an angleq_(r)(t) of a right hip joint of the user 120 using an encoderpositioned around the driver 210-2. When a left leg of the user 120moves forward as illustrated in FIG. 5 , the angle q_(l)(t) of the lefthip joint may be less than 0 and the angle q_(r)(t) of the right hipjoint may be greater than 0. However, according to an example, the angleq_(l)(t) of the left hip joint may be greater than 0 and the angleq_(r)(t) of the right hip joint may be less than 0.

Referring back to FIG. 4 , in operation 420, the wearable device 110defines a state variable. For example, the wearable device 110 defines astate variable y_(raw)(t) corresponding to a difference betweensin(q_(r)(t)) and sin(q_(l)(t)).

In operation 430, the wearable device 110 smooths the state variabley_(raw)(t). Through the smoothing, noise may be removed from the statevariable y_(raw)(t) and a waveform of the state variable y_(raw)(t) maybe smoothed. For example, the wearable device 110 performs low-passfiltering on the state variable y_(raw)(t). Equation 1 represents anexample of a result of the smoothing or a result of the low-passfiltering.

y(t)=(1−α)y(t _(prv))+αy _(raw)(t), (0<α1)  [Equation 1]

In Equation 1, y(t) denotes a smoothing result. In addition, a denotes asmoothing factor, and y(t_(prv)) denotes a previous smoothing result.

The smoothing result represented by Equation 1 is provided merely as anexample, and thus the smoothing result is not limited to what isrepresented by Equation 1 above. The smoothing result may vary based ona type of smoothing method, or a type of low-pass filter (LPF).

In operation 440, the wearable device 110 determines an interactive modeand a motion type of the wearable device 110 based on the motioninformation and a gain κ. The motion information may include, forexample, an angle q_(l)(t) of a left hip joint of the user 120 and anangle q_(r)(t) of a right hip joint of the user 120. The gain κ refersto a factor associated with a magnitude of a torque, and may be apositive or negative number. In addition, the gain κ may be received asan input from the user 120. For example, the user 120 may input the gainκ to a user interface (UI) device, for example, a tablet personalcomputer (PC), a smartphone, and the like, and the wearable device 110may receive the gain κ from the UI device. According to an example, theuser 120 may input the gain κ to the wearable device 110.

In an example, the wearable device 110 may determine one among aplurality of interactive modes based on the gain κ. The interactivemodes may be classified based on a type of a torque or force that isoutput by the wearable device 110 to the user 120. For example, theinteractive modes may include a first interactive mode that assists theuser 120 in a movement of the user 120, a second interactive mode thatapplies resistance to a movement of the user 120, and a thirdinteractive mode that applies high resistance to a movement of the user120.

In an example, the wearable device 110 may determine one among aplurality of motion types of the determined interactive mode based onthe motion information. The motion types may be classified based on aspeed of a movement of the user 120. For example, motion types of thefirst and second interactive modes may be different from motion types ofthe third interactive mode. The first and second interactive modes mayinclude, for example, a walk motion type, a walk-to-run motion type, anda run motion type. The third interactive mode may include ahigh-resistance motion type and a slow motion type.

The determining of an interactive mode and a motion type will bedescribed hereinafter in greater detail with reference to FIG. 6 .

Referring to FIG. 6 , in operation 610, the wearable device 110determines whether the gain κ is greater than or equal to a referencevalue r, or not.

In operation 620, in response to a determination that the gain κ isgreater than or equal to the reference value r, the wearable device 110determines whether the gain κ is a positive number or not.

In operation 630, in response to a determination that the gain κ is apositive number, the wearable device 110 selects the first interactivemode and determines a motion type of the wearable device 110 based on afirst gait feature value. The first gait feature value may include acadence of the user 120. The cadence may be calculated based on gaitinformation of the user 120 that includes, for example, a time used whenthe user 120 walks two steps forward and a walking distance when theuser 120 walks the two steps forward.

For example, when the reference value r is −5 (r=−5) and the gain κ is+5 (κ=+5), the wearable device 110 selects the first interactive mode.In addition, when the first gait feature value is less than or equal toa first threshold value (e.g., 120 which will be described hereinafterwith reference to FIG. 9 ), the wearable device 110 determines themotion type of the wearable device 110 to be the walk motion type. Whenthe first gait feature value is greater than the first threshold valueand less than or equal to a second threshold value (e.g., 140 which willbe described hereinafter with reference to FIG. 9 ), the wearable device110 determines the motion type of the wearable device 110 to be thewalk-to-run motion type. When the first gait feature value is greaterthan the second threshold value, the wearable device 110 determines themotion type of the wearable device 110 to be the run motion type. Thatis, when the cadence of the user 120 is less than a preset range, thewearable device 110 may determine that the user 120 is walking andselect the walk motion type. When the cadence of the user 120 is in thepreset range, the wearable device 110 may determine that the user 120 iswalking relatively fast and select the walk-to-run motion type. When thecadence of the user 120 is greater than the preset range, the wearabledevice 110 may determine that the user 120 is running and select the runmotion type.

In this example, the gain κ is a positive number, and thus the wearabledevice 110 may output a torque that assists the user 120 in his/hermovement irrespective of a motion type to be selected in the firstinteractive mode. Although to be described hereinafter, a control factormay vary for each motion type, and thus a magnitude of an assistancetorque may increase in sequential order starting from the walk motiontype to the walk-to-run motion type and then to the run motion type.

In operation 640, in response to a determination that the gain κ is anegative number, the wearable device 110 selects the second interactivemode and determines the motion type of the wearable device 110 based onthe first gait feature value. For example, when the reference value r is−5 (r=−5) and the gain κ is −3 (κ=−3), the wearable device 110 selectsthe second interactive mode. As described above in relation to operation630, the wearable device 110 determines one among the walk motion type,the walk-to-run motion type, and the run motion type based on the firstgait feature value. In this example, the gain κ is a negative number,and thus the wearable device 110 may output a torque that appliesresistance to a movement of the user 120 irrespective of a motion typeto be selected in the second interactive mode. Although to be describedhereinafter, a control factor may vary for each motion type, and thus amagnitude of a resistance torque may increase in sequential orderstarting from the walk motion type to the walk-to-run motion type andthen to the run motion type.

In operation 650, in response to a determination that the gain κ is lessthan the reference value r in operation 610, the wearable device 110selects the third interactive mode and determines the motion type of thewearable device 110 based on a second gait feature value. For example,when the reference value r is −5 (r=−5) and the gain κ is −7 (k=−7), thewearable device 110 selects the third interactive mode. In addition,when the second gait feature value is greater than a third thresholdvalue (e.g., 0.5 which will be described hereinafter with reference toFIG. 9 ), the wearable device 110 determines the motion type of thewearable device 110 to be the high-resistance motion type. When thesecond gait feature value is less than a fourth threshold value (e.g.,0.4 which will be described hereinafter with reference to FIG. 9 ), thewearable device 110 determines the motion type of the wearable device110 to be the slow motion type. That is, when the second gait featurevalue of the user 120 is greater than the third threshold value, thewearable device 110 may select the high-resistance motion type to applyhigh resistance to a movement of the user 120. When the second gaitfeature value of the user 120 is less than the fourth threshold value,the wearable device 110 may determine that the user 120 is walkingrelatively slow and select the slow motion type to apply an assistancetorque of a small magnitude to such a slow movement of the user 120.

The second gait feature value may refer to a value from which a gaitcharacteristic of the user 120, for example, a walking speed, during apreset period of time may be estimated. The second gait feature valuewill be described hereinafter in greater detail with reference to FIG. 8.

Referring back to FIG. 4 , in operation 450, the wearable device 110determines a control factor based on the determined motion type. Thecontrol factor may include at least one of a smoothing factor α or adelay Δt associated with an output timing of a torque. Although to bedescribed hereinafter, the control factor may affect a responsecharacteristic of a torque, and thus be referred to as a responsevariable or a sensing response variable. The determining of a controlfactor will be described hereinafter in greater detail with reference toFIG. 7 .

Referring to FIG. 7 , in operation 710, the wearable device 110 verifieswhether a motion type change event occurs. For example, the wearabledevice 110 verifies whether the motion type determined in operation 440is the same as a previous motion type. In this example, when the motiontype determined in operation 440 is not the same as the previous motiontype, the motion type change event may occur. In contrast, when themotion type determined in operation 440 is the same as the previousmotion type, the motion type change event may not occur.

In operation 720, when the motion type change event occurs, the wearabledevice 110 adjusts the control factor. For example, when the previousmotion type is the run motion type and the walk motion type isdetermined in operation 440, the motion type change event occurs, andthus the wearable device 110 changes a previously set control factor,for example, a control factor corresponding to the run motion type, to acontrol factor corresponding to the walk motion type. In an example,each motion type and a corresponding control factor may be mapped toeach other in a lookup table. The adjusting of a control factor based ona motion type will be described hereinafter in greater detail withreference to FIG. 9 .

In operation 730, when the motion type change event does not occur, thewearable device 110 maintains the previously set control factor.

Referring back to FIG. 4 , in operation 460, the wearable device 110schedules a compensation factor κ_(comp) based on the determined motiontype. The compensation factor κ_(comp) refers to a factor to be used tocompensate for a magnitude of a torque. In addition, the compensationfactor κ_(comp) may be used to adjust or compensate for linearresponsiveness to torque generation for each motion type.

For example, when the run motion type is determined in operation 440,the wearable device 110 may determine the compensation factor κ_(comp)to be 1.2. When the walk motion type is determined in operation 440, thewearable device 110 may determine the compensation factor κ_(comp) tobe 1. When the high-resistance motion type is determined in operation440, the wearable device 110 may determine the compensation factorκ_(comp) to be 0.8. When the slow motion type is determined in operation440, the wearable device 110 may determine the compensation factorκ_(comp) to be −5/κ. In an example, each motion type and a correspondingcompensation factor may be mapped to each other in a lookup table.

In operation 470, the wearable device 110 generates a torque based onthe smoothing result y(t), the gain κ, the control factor, and thecompensation factor κ_(comp). An example of torque t(t) may berepresented by Equation 2.

τ(t)=κ·κ_(comp) y(t−Δt)  [Equation 2]

For example, the wearable device 110 may generate an assistance torquein each motion type of the first interactive mode. In this example, acontrol factor may vary based on each motion type, and thus anassistance torque in the walk motion type may be smaller than anassistance torque in the walk-to-run motion type, and the assistancetorque in the walk-to-run motion type may be smaller than an assistancetorque in the run motion type, even though the gain κ is the same inthose motion types.

For another example, the wearable device 110 may generate a resistancetorque in each motion type of the second interactive mode. In thisexample, a control factor may vary based on each motion type, and thus aresistance torque in the walk motion type may be smaller than aresistance torque in the walk-to-run motion type, and the resistancetorque in the walk-to-run motion type may be smaller than a resistancetorque in the run motion type, even though the gain κ is the same inthose motion types.

For still another example, wearable device 110 may generate ahigh-resistance torque in the high-resistance motion type of the thirdinteractive mode. In this example, the high-resistance torque may bestronger or greater than the resistance torque in the second interactivemode. In addition, the wearable device 110 may generate an assistancetorque of a relatively small magnitude to assist the user 120 in slowwalking in the slow motion type of the third interactive mode.

FIG. 8 is a diagram illustrating an example of a second gait featurevalue according to at least one example embodiment.

FIG. 8 illustrates an example of an angle q_(r)(t) of a right hip jointof the user 120.

A second gait feature value may be a value from which a gaitcharacteristic of the user 120 for a desired (or, alternatively, apreset) period of time may be estimated. For example, the second gaitfeature value may be calculated based on each of angular curve lengthsof both hip joints for a recent 1 second. In the example of FIG. 8 , thewearable device 110 calculates an angular curve length q_(r_length) 810of the angle q_(r)(t) of the right hip joint during an interval betweent−1 and t as represented by Equation 3.

q _(length)=∫_(t-1) ^(t) q(t)dt−1≈Σ_(t-1) ^(t)√{square root over ((t−t_(prv))²+(q−q _(prv))²)}−1  [Equation 3]

In Equation 3, √{square root over ((t−t_(prv))²+(q−q_(prv))²)} denotes adistance between a point (t, q) and a neighboring point (t_(prv),q_(prv)). Based on Equation 3, the wearable device 110 calculates eachof a distance between a point 820-1 and a point 820-2, a distancebetween the point 820-2 and a point 820-3, a distance between the point820-3 and a point 820-4, a distance between the point 820-4 and a point820-5, a distance between the point 820-5 and a point 820-6, a distancebetween the point 820-6 and a point 820-7, and a distance between thepoint 820-7 and a point 820-8, a distance between the point 820-8 and apoint 820-9, and a distance between the point 820-9 and a point 820-10.The wearable device 110 calculates the angular curve length q_(r_length)810 by subtracting 1 from a sum of the calculated distances.

Although not illustrated in FIG. 8 , the wearable device 110 may alsocalculate an angular curve length q_(l_length) of an angle q_(l)(t) of aleft hip joint of the user 120 during the interval between t−1 and t, asdescribed above.

The wearable device 110 may calculate a mean value of the angular curvelengths g_(r_length) and q_(l_length), and determine the mean value tobe the second gait feature value.

FIG. 9 is a diagram illustrating an example of a state machine of thewearable device 110 according to at least one example embodiment.

FIG. 9 illustrates an example of a state machine when the referencevalue r is −5 (r=−5).

For example, when the gain κ is greater than or equal to −5, thewearable device 110 selects the first or second interactive mode. Inthis example, when the gain κ is a positive number, the wearable device110 selects the first interactive mode. In contrast, when the gain κ isa negative number, the wearable device 110 selects the secondinteractive mode. In addition, when the gain κ is less than −5, thewearable device 110 selects the third interactive mode.

<In a Case of the Gain κ being Greater than or Equal to −5>

Referring to FIG. 9 , when a cadence of the user 120 reaches between 120to 140 while the wearable device 110 is operating in a walk motion type910, the wearable device 110 changes the walk motion type 910 to awalk-to-run motion type 920. As the motion type changes, the wearabledevice 110 adjusts a control factor. That is, since the walk-to-runmotion type 920 is not the same as the walk motion type 910 which is aprevious motion type, the wearable device 110 adjusts the controlfactor. For example, the wearable device 110 may increase a smoothingfactor α and decrease a delay Δt. In the example of FIG. 9 , thewearable device 110 increases the smoothing factor from 0.05 to a valuewithin a range from 0.05 to 0.10, and decreases the delay from 0.25 to avalue within a range from 0.20 to 0.25. Conventionally, when the cadenceof the user 120 reaches between 120 to 140 while operating in a walkmotion type, saturation or torque attenuation in which a torque does notincrease in proportion to a walking speed may occur due to smoothing orlow-pass filtering. In contrast, in one or more example embodiments, bychanging to the walk-to-run motion type 920 when the cadence is reachesbetween 120 to 140, smoothing may be performed with the adjustedsmoothing factor and a torque may be generated with the adjusted delay,and thus the saturation may be reduced or minimized, and an attenuatedamount of the torque may be compensated for.

In the walk-to-run motion type 920, the smoothing factor and the delaymay be set values within respective ranges. However, the values are notlimited to such illustrated examples. For example, in the walk-to-runmotion type 920, the smoothing factor and the delay may be valuesmatched to a cadence of the user 120 within respective ranges. In thisexample, when the cadence of the user 120 is 120, the smoothing factormay be 0.055 and the delay may be 0.205. When the cadence of the user120 is 130, the smoothing factor may be 0.075 and the delay may be0.225. When the cadence of the user 120 is 140, the smoothing factor maybe 0.095 and the delay may be 0.245.

When the cadence of the user 120 reaches 140 while the wearable device110 is operating in the walk motion type 910, the wearable device 110changes the walk motion type 910 to a run motion type 930. As the motiontype changes, the wearable device 110 adjusts a control factor. Forexample, the wearable device 110 may increase a smoothing factor α anddecrease a delay Δt. In the example of FIG. 9 , the wearable device 110increases the smoothing factor from 0.05 to 0.1 and decrease the delayfrom 0.25 to a value within a range from 0.15 to 0.20. In addition, thewearable device 110 adjusts a compensation factor romp from 1 to 1.2.Conventionally, when the cadence of the user 120 exceeds 140 whileoperating in walk motion type, torque attenuation may occur due tosmoothing or low-pass filtering. In contrast, in one or more exampleembodiments, by changing to the run motion type 930 when the cadence isgreater than or equal to 140, smoothing may be performed with theadjusted smoothing factor, and a torque may be generated with theadjusted delay and the adjusted compensation factor. Thus, an attenuatedamount of the torque may be compensated for.

When the cadence of the user 120 reaches 120 while the wearable device110 is operating in the walk-to-run motion type 920, the wearable device110 changes the walk-to-run motion type 920 to the walk motion type 910.As the motion type changes, the wearable device 110 adjusts a controlfactor. For example, wearable device 110 may decrease a smoothing factorand increase a delay. In the example of FIG. 9 , the wearable device 110decreases the smoothing factor to 0.05 and increases the delay to 0.25.

When the cadence of the user 120 reaches 150 or greater while thewearable device 110 is operating in the walk-to-run motion type 920, thewearable device 110 changes the walk-to-run motion type 920 to the runmotion type 930. As the motion type changes, the wearable device 110adjusts a control factor. For example, the wearable device 110 mayincrease a smoothing factor and decrease a delay. In addition, thewearable device 110 may increase a compensation factor. When the cadenceof the user 120 exceeds 140, torque attenuation may occur as describedabove. Thus, the wearable device 110 may perform smoothing using theadjusted smoothing factor and generate a torque using the adjusted delayand the adjusted compensation factor such that the torque attenuationmay not occur when the walk-to-run motion type 920 changes to the runmotion type 930.

When the cadence of the user 120 reaches 120 while the wearable device110 is operating in the run motion type 930, the wearable device 110changes the run motion type 930 to the walk motion type 910. As themotion type changes, the wearable device 110 adjusts a control factor.For example, the wearable device 110 may decrease a smoothing factor andincrease a delay. In addition, the wearable device 110 may adjust acompensation factor from 1.2 to 1.

As described above, a control factor may vary for each of the motiontypes 910, 920, and 930. That is, with a same gain κ, a smoothing factorα may be adjusted to increase and a delay Δt may be adjusted todecrease, in sequential order starting from the walk motion type 910 tothe walk-to-run motion type 920 and then to the run motion type 930. Byadjusting the control factor as described in the foregoing, a magnitudeof a torque may increase linearly and stably as a motion speed of theuser 120 increases. In addition, the magnitude of the torque mayincrease linearly and stably as the gain increases linearly. Thus, alinear response characteristic and control stability of the wearabledevice 110 may be improved.

<In a Case of the Gain κ being Less than −5>

When q_(length) is less than 0.4 while the wearable device 110 isoperating in a high-resistance motion type 940, the wearable device 110changes the high-resistance motion type 940 to a slow motion type 950.That is, the wearable device 110 changes the high-resistance motion type940 to the slow motion type 950 when q_(length) is less than 0.4, andthe wearable device 110 maintains the high-resistance motion type 940when q_(length) is less than 0.5 and greater than or equal to 0.4 whilethe wearable device 110 is operating in the high-resistance motion type940. In the example of FIG. 9 , although the high-resistance motion type940 changes to the slow motion type 950, a smoothing factor and a delaymay not change. Similarly, also in a case in which the slow motion type950 changes to the high-resistance motion type 940, the smoothing factorand the delay may not change. However, examples are not limited to whatis described in the foregoing, and at least one of the smoothing factoror the delay may be configured to change in response to a change inmotion type.

When a motion speed of the user 120 increases in the high-resistancemotion type 940, a magnitude of a torque may also increase linearly andstably. In addition, when a gain increases linearly, the magnitude ofthe torque may also increase linearly and stably. Thus, in thehigh-resistance motion type 940, a linear response characteristic andcontrol stability of the wearable device 110 may be improved.

As discussed above, conventionally, when the cadence of the user 120reaches a threshold while operating in a walk motion type, saturation ortorque attenuation in which a torque does not increase in proportion toa walking speed may occur due to smoothing or low-pass filtering andwhen the cadence of the user 120 exceeds a second threshold higher thanthe first threshold while operating in the walk motion type, torqueattenuation may occur due to smoothing or low-pass filtering.

In contrast, in one or more example embodiments, when the gain κ isgreater than the reference value, the wearable device 110 may operate ina first or second reference mode to adjust a control factor (e.g., asmoothing factor α and a delay Δt) by switching between a walking motiontype 910, a walk to run motion type 920 and a run motion type 930 basedon the cadence such that the smoothing factor increases a and the delayΔt decreases in sequential order as the cadence increases from the walkmotion type 910 to the walk-to-run motion type 920 and then to the runmotion type 930. Therefore, in the first interactive mode (i.e., theassistance mode when the gain κ is positive), a magnitude of theassistance torque may increase linearly and stably as the cadence of theuser 120 increases during movement between the motion types 910-930 and,in the second interactive mode (i.e., the resistance mode when the gainκ is negative), a magnitude of the resistance torque may increaselinearly and stably as the cadence of the user 120 increases duringmovement between the motion types 910-930, even though the gain κremains the same in the motion types. Further, when the gain κ is lessthan the reference value, the wearable device 110 may operate in a thirdreference mode to maintain a constant control factor (e.g., thesmoothing factor α and the delay Δt) in both the high-resistance motiontype 940 and the slow motion type 950 such that the smoothing factor αis relatively high to generate a stronger resistance torque orrelatively smaller assistance torque while the delay Δt is relativelyshort as compared to the second interactive mode. Thus, a linearresponse characteristic and control stability of the wearable device 110may be improved.

FIG. 10 is a flowchart illustrating an example of an operation method ofthe wearable device 110 according to at least one example embodiment.

Referring to FIG. 10 , in operation 1010, the wearable device 110processes a state variable defined based on motion information of theuser 120. For example, the wearable device 110 may smooth a statevariable y_(raw)(t).

In operation 1020, the wearable device 110 determines an interactivemode of the wearable device 110 based on a gain associated with amagnitude of a torque, where the gain may be input by the user. In someother example embodiments, rather than the user inputting the gain andthe controller 310 determining the interactive mode based on the inputgain, the user may input the interactive mode, and the controller 310may determine a default gain associated with the input interactive mode.

In operation 1030, the wearable device 110 selects one from among motiontypes of the determined interactive mode based on a gait parameter ofthe user 120. The gait parameter may include a plurality of gait featurevalues that represent gait or walking characteristics. The gaitparameter may include, for example, the first and second gait featurevalue described above.

In operation 1040, the wearable device 110 determines a control factorfor a torque based on the selected motion type. For example, thewearable device 110 may search a lookup table for a control factorcorresponding to the selected motion type. For another example, thewearable device 110 may determine the control factor corresponding tothe selected motion type through a regression function or a regressionanalysis. In an example, a control factor for each motion type may beoptimized through training.

In operation 1050, the wearable device 110 generates the torque based onthe processed state variable, the gain, and the determined controlfactor. The processed state variable may correspond to y(t) describedabove. The wearable device 110 may generate the torque by applying adifferent control factor to each motion type. Through this, a linearresponse characteristic and control stability of the wearable device 110may be improved.

For a detailed description of the operation method described above withreference to FIG. 10 , reference may be made to what has been describedabove with reference to FIGS. 1 through 9 .

FIG. 11 is a diagram illustrating an example of the wearable device 110according to at least one example embodiment.

Referring to FIG. 11 , the wearable device 110 includes the controller310 and a driver 1110.

Further, the wearable apparatus 110 may include a user interface (UI)device and one or more sensors. The UI device may be configured toreceive an input of a gain associated with a magnitude of a torque fromthe user. The UI device may include various appropriate devices, forexample, a switch, a knob, and a jog dial, configured to set theexercise mode. The UI device may be replaced with an external remotecontrol or a smart device and may not need to be included in thewearable apparatus 110. The sensors may be angle sensors, for example, apotentiometer, an absolute encoder, or an incremental encoder,configured to measure an angle of a joint.

The controller 310 may be implemented in processing circuitry such ashardware including logic circuits; a hardware/software combination suchas a processor executing software; or a combination thereof and memory.For example, the processing circuitry more specifically may include, butis not limited to, a central processing unit (CPU), an arithmetic logicunit (ALU), a digital signal processor, a microcomputer, a fieldprogrammable gate array (FPGA), a programmable logic unit, amicroprocessor, application-specific integrated circuit (ASIC), etc. Theprocessing circuitry may be special purpose processing circuitry thatperforms an overall operation of the wearable device 110 that has beendescribed above with reference to FIGS. 1 through 10 . For example, thecontroller 310 may process a state variable defined based on motioninformation of the user 120, and determine an interactive mode of thewearable device 110 based on a gain associated with a magnitude of atorque, where the gain may be input by the user 120 via a user interface(UI) device. In some other example embodiments, rather than the userinputting the gain and the controller 310 determining the interactivemode based on the input gain, the user may input the interactive mode,and the controller 310 may determine a default gain associated with theinput interactive mode. In addition, the controller 310 may select onefrom among motion types of the determined interactive mode based on agait parameter of the user 120, and determine a control factor for atorque based on the selected motion type. The controller 310 may thencontrol the driver 1110 based on the processed state variable, the gain,and the determined control factor. Thus, the processing circuitry mayimprove the functioning of the wearable apparatus 110 itself by linearlyand stably increasing a magnitude of a torque as a motion speed of theuser 120 increases to improve linear response characteristic and controlstability of the wearable device 110.

The driver 1110 may generate the torque under the control of thecontroller 310.

The wearable device 110 may include a single driver, for example, thedriver 1110 as illustrated in FIG. 11 , or include a plurality ofdrivers, for example, the drivers 210-1 and 210-2 as illustrated inFIGS. 2 and 3 .

For a detailed description of the wearable device 110 described abovewith reference to FIG. 11 , reference may be made to what has beendescribed above with reference to FIGS. 1 through 10 .

The units and/or modules described herein may be implemented usinghardware components and software components. For example, the hardwarecomponents may include microphones, amplifiers, band-pass filters, audioto digital convertors, and processing devices. A processing device maybe implemented using one or more hardware device configured to carry outand/or execute program code by performing arithmetical, logical, andinput/output operations. The processing device(s) may include aprocessor, a controller and an arithmetic logic unit, a digital signalprocessor, a microcomputer, a field programmable array, a programmablelogic unit, a microprocessor or any other device capable of respondingto and executing instructions in a defined manner. The processing devicemay run an operating system (OS) and one or more software applicationsthat run on the OS. The processing device also may access, store,manipulate, process, and create data in response to execution of thesoftware. For purpose of simplicity, the description of a processingdevice is used as singular; however, one skilled in the art willappreciated that a processing device may include multiple processingelements and multiple types of processing elements. For example, aprocessing device may include multiple processors or a processor and acontroller. In addition, different processing configurations arepossible, such a parallel processors.

The software may include a computer program, a piece of code, aninstruction, or some combination thereof, to independently orcollectively instruct and/or configure the processing device to operateas desired, thereby transforming the processing device into a specialpurpose processor. Software and data may be embodied permanently ortemporarily in any type of machine, component, physical or virtualequipment, computer storage medium or device, or in a propagated signalwave capable of providing instructions or data to or being interpretedby the processing device. The software also may be distributed overnetwork coupled computer systems so that the software is stored andexecuted in a distributed fashion. The software and data may be storedby one or more non-transitory computer readable recording mediums.

The methods according to the above-described example embodiments may berecorded in non-transitory computer-readable media including programinstructions to implement various operations of the above-describedexample embodiments. The media may also include, alone or in combinationwith the program instructions, data files, data structures, and thelike. The program instructions recorded on the media may be thosespecially designed and constructed for the purposes of exampleembodiments, or they may be of the kind well-known and available tothose having skill in the computer software arts. Examples ofnon-transitory computer-readable media include magnetic media such ashard disks, floppy disks, and magnetic tape; optical media such asCD-ROM discs, DVDs, and/or Blue-ray discs; magneto-optical media such asoptical discs; and hardware devices that are specially configured tostore and perform program instructions, such as read-only memory (ROM),random access memory (RAM), flash memory (e.g., USB flash drives, memorycards, memory sticks, etc.), and the like. Examples of programinstructions include both machine code, such as produced by a compiler,and files containing higher level code that may be executed by thecomputer using an interpreter. The above-described devices may beconfigured to act as one or more software modules in order to performthe operations of the above-described example embodiments, or viceversa.

A number of example embodiments have been described above. Nevertheless,it should be understood that various modifications may be made to theseexample embodiments. For example, suitable results may be achieved ifthe described techniques are performed in a different order and/or ifcomponents in a described system, architecture, device, or circuit arecombined in a different manner and/or replaced or supplemented by othercomponents or their equivalents. Accordingly, other implementations arewithin the scope of the following claims.

What is claimed is:
 1. An operation method of a wearable device, theoperation method comprising: processing a state variable to generate aprocessed state variable, the state variable being based on motioninformation of a user; determining an interactive mode of the wearabledevice based on a gain associated with a magnitude of a torque of thewearable device; selecting a motion type from among a plurality ofmotion types associated with the interactive mode based on a gaitparameter of the user; determining a control factor for the torque basedon the motion type; and generating the torque based on the processedstate variable, the gain, and the control factor.
 2. The operationmethod of claim 1, wherein the processing of the state variablecomprises: smoothing the state variable to generate the processed statevariable.
 3. The operation method of claim 1, wherein the determining ofthe interactive mode comprises: selecting a first interactive mode thatassists the user in a movement of the user, in response to the gainbeing a positive number that is greater than or equal to a referencevalue; selecting a second interactive mode that applies resistance to amovement of the user, in response to the gain being a negative numberthat is greater than or equal to the reference value; and selecting athird interactive mode that applies high resistance to a movement of theuser, in response to the gain being less than the reference value. 4.The operation method of claim 1, wherein the gait parameter includes afirst gait feature value, and the selecting of the motion typecomprises: determining the motion type of the wearable device to be awalk motion type, in response to the first gait feature value being lessthan or equal to a first threshold value; determining the motion type tobe a walk-to-run motion type, in response to the first gait featurevalue being greater than the first threshold value and less than orequal to a second threshold value; and determining the motion type to bea run motion type, in response to the first gait feature value beinggreater than the second threshold value.
 5. The operation method ofclaim 4, wherein the first gait feature value is a cadence of the user.6. The operation method of claim 1, wherein the gait parameter includesa first gait feature value and a second gait feature value, and theselecting of the motion type comprises: determining the motion type ofthe wearable device to be a high-resistance motion type, in response tothe second gait feature value in the gait parameter being greater than athird threshold value; and determining the motion type to be a slowmotion type, in response to the second gait feature value being lessthan a fourth threshold value.
 7. The operation method of claim 6,wherein the second gait feature value is a mean value of angular curvelengths of hip joints of the user during a set period of time.
 8. Theoperation method of claim 1, wherein the determining of the controlfactor comprises: adjusting at least one of a smoothing factorassociated with the processing of the state variable and a delay inoutput timing of the torque, in response to a motion type change eventoccurring by the selecting of the motion type.
 9. The operation methodof claim 8, wherein the adjusting comprises: decreasing the smoothingfactor and increasing the delay, in response to the motion type changeevent occurring by the selecting of a walk motion type from among theplurality of motion types.
 10. The operation method of claim 8, whereinthe adjusting comprises: increasing the smoothing factor and decreasingthe delay, in response to the motion type change event occurring by theselecting of a run motion type from among the plurality of motion types.11. The operation method of claim 1, wherein the generating of thetorque comprises: set a torque value by applying, to the processed statevariable, the gain, the control factor, and a compensation factor; andgenerating the torque based on the torque value.
 12. The operationmethod of claim 1, wherein the motion information includes angles of hipjoints of the user.
 13. A wearable device comprising: a driverconfigured to generate a torque; and a controller configured to, processa state variable to generate a processed state variable, the statevariable being based on motion information of a user, determine aninteractive mode of the wearable device based on a gain associated witha magnitude of the torque of the wearable device, select a motion typefrom among a plurality of motion types associated with the interactivemode based on a gait parameter of the user, determine a control factorfor the torque based on the motion type, and control the driver based onthe processed state variable, the gain, and the control factor.
 14. Thewearable device of claim 13, wherein the controller is configured toprocess the state variable by smoothing the state variable to generatethe processed state variable.
 15. The wearable device of claim 13,wherein the controller is configured to: select a first interactive modethat assists the user in a movement of the user, in response to the gainbeing a positive number greater than or equal to a reference value,select a second interactive mode that applies resistance to a movementof the user, in response to the gain being a negative number greaterthan or equal to the reference value, and select a third interactivemode that applies high resistance to a movement of the user, in responseto the gain being less than the reference value.
 16. The wearable deviceof claim 13, wherein the gait parameter includes a first gait featurevalue, and the controller is configured to: determine the motion type ofthe wearable device to be a walk motion type, in response to the firstgait feature value being less than or equal to a first threshold value,determine the motion type to be a walk-to-run motion type, in responseto the first gait feature value being greater than the first thresholdvalue and less than or equal to a second threshold value, and determinethe motion type to be a run motion type, in response to the first gaitfeature value being greater than the second threshold value.
 17. Thewearable device of claim 16, wherein the first gait feature value is acadence of the user.
 18. The wearable device of claim 13, wherein thegait parameter includes a first gait feature value and a second gaitfeature value, and the controller is configured to: determine the motiontype of the wearable device to be a high-resistance motion type, inresponse to the second gait feature value being greater than a thirdthreshold value, and determine the motion type to be a slow motion type,in response to the second gait feature value being less than a fourththreshold value.
 19. The wearable device of claim 18, wherein the secondgait feature value is a mean value of angular curve lengths of hipjoints of the user during a set period of time.
 20. The wearable deviceof claim 13, wherein the controller is configured to: adjust at leastone of a smoothing factor associated with the processing of the statevariable or a delay in output timing of the torque, in response to amotion type change event occurring by the controller selecting themotion type.
 21. The wearable device of claim 20, wherein the controlleris configured to: decrease the smoothing factor and increase the delay,in response to the motion type change event occurring by the controllerselecting a walk motion type from among the plurality of motion types.22. The wearable device of claim 20, wherein the controller isconfigured to: increase the smoothing factor and decrease the delay, inresponse to the motion type change event occurring by the controllerselecting a run motion type from among the plurality of motion types.23. The wearable device of claim 13, wherein the controller isconfigured to: set a torque value by applying, to the processed statevariable, the gain, the control factor, and a compensation factor. 24.The wearable device of claim 13, wherein the motion information includesangles of hip joints of the user.